********************************************************************************
* REPLICATION MATERIAL FOR THE PAPER: VERHASSELT & PAULIS 'DIVERSE VOICES, COMMON GOALS: THE IMPACT OF MULTILINGUALISM ON PUBLIC SUPPORT FOR DELIBERATIVE MINI-PUBLICS'******************************************************
********************************************************************************
********* IMPORT DATA **********************************************************
import excel "/Users/XXX/multilingual_paper_replication.xlsx", sheet("wave1") firstrow

********************************************************************************
********************************************************************************
**** MODEL 1: ANALYSIS OF WAVE 1 DATA ONLY *************************************
********************************************************************************
********************************************************************************

********************************************************************************
*** DV: SUPPORT FOR MULTILINGUAL DMPs ******************************************
********************************************************************************

*'Citizens' Assemblies like the KBR cannot work properly in a multilingual setting like Luxembourg', where 1=strongly agree; 5=strongly disagree 
* keep raw coding in order to have higher value = more support for multilingual CAs
tab multi_delib
recode multi_delib (6=.) 
label define multi_delib 1 "Completely agree" 2 "Agree" 3 "Neutral" 4 "Disagree" 5 "Completely disagree", modify
label values multi_delib multi_delib
tab multi_delib

********************************************************************************
*** IV I : MULTILINGUAL SKILLS *************************************************
********************************************************************************

** Operationalization #1 : additive scale
gen lux_bin=.
replace lux_bin = 0 if luxembourgish < 3
replace lux_bin= 1 if luxembourgish >= 3

gen french_bin=.
replace french_bin = 0 if french < 3
replace french_bin= 1 if french >= 3

gen german_bin=.
replace german_bin = 0 if german < 3
replace german_bin= 1 if german >= 3

gen english_bin=.
replace english_bin = 0 if english < 3
replace english_bin= 1 if english >= 3

gen other_bin=.
replace other_bin = 0 if otherlanguages < 3
replace other_bin= 1 if otherlanguages >= 3

gen multilingual_skills = (lux_bin + french_bin + german_bin + english_bin + other_bin)

** Operationalization #2 : factor analysis
factor luxembourgish french german english otherlanguages
rotate, varimax blanks (.3)
predict factor*
loadingplot

*factor1 = Luxembourgish & German / factor2 = French, English & other languages
rename factor1 native_speakers
rename factor2 foreign_speakers

********************************************************************************
*** IV II : NATIONALITY ********************************************************
********************************************************************************

** nationality
gen nationality=.
replace nationality = 0 if citizenship1 == 2 & citizenship2 == 1
replace nationality = 1 if citizenship1 == 1 & citizenship2 == 1
replace nationality = 2 if citizenship1 == 1 & citizenship2 == 2
label define nationality 0 "non-national" 1 "Luxembourg nationality + other" 2 "Luxembourg nationality only"
label values nationality nationality
tab nationality

********************************************************************************
*** CONTROL VARIABLES **********************************************************
********************************************************************************

*** CV I: SOCIO - DEMO *********************************************************

** sex
tab gender
label drop gender
label define gender 1 "Male" 2 "Female"
label values gender gender 
tab gender

** education
tab edu
recode edu (1=1)(2=1)(3=1)(4=2)(5=2)(6=2)(7=3)(8=3)(9=3)(997=.)
label define edu 1 "Low" 2 "Middle" 3 "High" 
label values edu edu
tab edu
rename edu education

****** CV II: POLITICAL ATTITUDES **********************************************

** interest: how interested would you say you are in politics
* '1' very interested; '4' not interested at all / 5 don't know
recode interest (1=4) (2=3) (3=2) (4=1) (5=.)
label define interest 1 "not at all interested" 2 "not very interested" 3 "somewhat interested" 4 "very interested", modify
label values interest interest
tab interest

** satisfaction w/ democracy
recode swd (99=.)
tab swd

** left/right self-placement
recode lrsp (99=.)
tab lrsp

** cultural left/right 
* 'Luxembourg is made a worse place to live by people coming to live here from other countries', where '1' fully agree / '5' fully disagree (6 = don't know)
* recoding in order to have higher value = more right-wing/conservative
recode cult_lr (6=.)(1=5)(2=4)(3=3)(4=2) (5=1)
label define cult_lr 1 "Completely disagree" 2 "Disagree" 3 "Neutral" 4 "Agree" 5 "Completely agree", modify
label values cult_lr cult_lr
tab cult_lr

** internal efficacy 
*'politics is too complicated for people like me' where '1' fully agree / '5' fully disagree (6 = don't know)
* keep raw coding: higher value = feel more efficacious
tab efficacy_perso
recode efficacy_perso (6=.)
label define efficacy_perso 1 "Completely agree" 2 "Agree" 3 "Neutral" 4 "Disagree" 5 "Completely disagree", modify
label values efficacy_perso efficacy_perso
tab efficacy_perso

** trust in representative institutions, 
* 'how much do you trust the following actors' (a) parties (b) politicians (c) parlaiment, where '1' = no trust at all and '5' = high trust (6= don't know)
* keep raw coding: higher value = more trust
recode trust1 (6=.)
recode trust2 (6=.)
recode trust3 (6=.)

* build a scale of trust in representative institutions
alpha trust1 trust2 trust3, gen(trust)

****** CV III: ATTITUDES TOWARDS DMPs in general *******************************

** support for DMPs
* 'Citizens Assemblies like the KBR should be organized on other issues', where '1' fully agree / '5' fully disagree (6 = don't know)
* recode in order to have higher value = more support
tab dmp_support
recode dmp_support (6=.)(1=5)(2=4)(3=3)(4=2)(5=1)
label define dmp_support 1 "Completely disagree" 2 "Disagree" 3 "Neutral" 4 "Agree" 5 "Completely agree", modify
label values dmp_support dmp_support
tab dmp_support

********************************************************************************
***** M1: LINEAR REGRESSION MODEL **********************************************
********************************************************************************

* model w/ multilingual skills operationalization #1
reg multi_delib multilingual_skills
outreg2 using resultsM1.doc, replace ctitle (M1) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib multilingual_skills gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support
estimates store M1a_no
outreg2 using resultsM1.doc, append ctitle (M1 without nationality) alpha(0.001, 0.01, 0.05) stats(coef se)
reg multi_delib multilingual_skills i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support
estimates store M1a
outreg2 using resultsM1.doc, append ctitle (M1 with nationality) alpha(0.001, 0.01, 0.05) stats(coef se)

* model w/ multilingual skills operationalization #2
reg multi_delib native_speakers foreign_speakers
outreg2 using resultsM1.doc, append ctitle (M1) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib native_speakers foreign_speakers gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support
estimates store M1b_no
outreg2 using resultsM1.doc, append ctitle (M1 without nationality) alpha(0.001, 0.01, 0.05) stats(coef se)
reg multi_delib native_speakers foreign_speakers i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support
estimates store M1b
outreg2 using resultsM1.doc, append ctitle (M1 with nationality) alpha(0.001, 0.01, 0.05) stats(coef se)

coefplot M1a_no M1a || M1b_no M1b, xline(0) drop(_cons gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support) headings(multilingual_skills = "{it:Multilingual skills #1:}" native_speakers = "{it:Multilingual skills #2:}" 0.nationality = "{it:Nationality:}", nogap) baselevel 

********************************************************************************
****** M1: ROBUSTNESS CHECKS ***************************************************
********************************************************************************

*** weighted data
reg multi_delib multilingual_skills i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support [aweight= d_weight]
outreg2 using resultsM1-robust.doc, replace ctitle (M1 weigthed) alpha(0.001, 0.01, 0.05) stats(coef se)

reg multi_delib native_speakers foreign_speakers i.nationality gender age education  interest swd lrsp cult_lr efficacy_perso trust dmp_support [aweight= d_weight]
outreg2 using resultsM1-robust.doc, append ctitle (M1 weigthed) alpha(0.001, 0.01, 0.05) stats(coef se)

*** ordinal logits
ologit multi_delib multilingual_skills i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support
outreg2 using resultsM1-robust.doc, append ctitle (M1 ordinal) alpha(0.001, 0.01, 0.05) stats(coef se)

ologit multi_delib native_speakers foreign_speakers i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support
outreg2 using resultsM1-robust.doc, append ctitle (M1 ordinal) alpha(0.001, 0.01, 0.05) stats(coef se)

*** ordinal logits & wieghted data
ologit multi_delib multilingual_skills i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support [aweight= d_weight]
outreg2 using resultsM1-robust.doc, append ctitle (M1 ordinal & weighted) alpha(0.001, 0.01, 0.05) stats(coef se)

ologit multi_delib native_speakers foreign_speakers i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support [aweight= d_weight]
outreg2 using resultsM1-robust.doc, append ctitle (M1 ordinal & weighted) alpha(0.001, 0.01, 0.05) stats(coef se)

********************************************************************************
******* M1: INTERPRETATION TESTS ***********************************************
********************************************************************************

** Inclusive DMPs
*`Citizens' Assemblies like the KBR should involve only Luxembourg Nationals, and not residents who are not Luxembourg Nationals'
* where '1'=fully agree and '5' fully disagree >> higher value, more support for inclusive DMPs
recode inclusive_delib (6=.)
reg inclusive_delib native_speakers foreign_speakers gender age edu interest swd lrsp cult_lr efficacy_perso trust dmp_support
outreg2 using results-interpretation.doc, replace ctitle (Inclusive DMP - without nationality) alpha(0.001, 0.01, 0.05) stats(coef se)
reg inclusive_delib native_speakers foreign_speakers i.nationality gender age edu  interest swd lrsp cult_lr efficacy_perso trust dmp_support
outreg2 using results-interpretation.doc, append ctitle (Inclusive DMP - with nationality) alpha(0.001, 0.01, 0.05) stats(coef se)

** Participation in future DMPs
*`If retained to be member of a Citizens' Assembly or public consultation on climate change or any other important issue in the future, how likely is it that you would participate?'
* 0 very unlikely, 10 very likely 
recode participation (-99=.)
reg participation native_speakers foreign_speakers gender age edu interest swd lrsp cult_lr efficacy_perso trust dmp_support
outreg2 using results-interpretation.doc, append ctitle (DMP participation - without nationality) alpha(0.001, 0.01, 0.05) stats(coef se)
reg participation native_speakers foreign_speakers i.nationality gender age edu  interest swd lrsp cult_lr efficacy_perso trust dmp_support
outreg2 using results-interpretation.doc, append ctitle (DMP participation - with nationality) alpha(0.001, 0.01, 0.05) stats(coef se)

********************************************************************************
********************************************************************************
********** MODEL 2: ANALYSIS OF WAVE 2 DATA ONLY *******************************
********************************************************************************
********************************************************************************

import excel "/Users/XXX/multilingual_paper_replication.xlsx", sheet("wave2") firstrow

********************************************************************************
***** DV: SUPPORT FOR MULTILINGUAL CAs *****************************************
********************************************************************************
* post treatment question 'do you think that CAs like the KBR can properly work in multilignual context like Luxembourg?, where '1' = strongly agree and '5' = strongly disagree ('6'= don't know)
* reverse in order to have higer value = more positive
recode multi_delib_w2 (6=.)
tab multi_delib_w2
revrs multi_delib_w2
tab revmulti_delib_w2
drop multi_delib_w2
rename revmulti_delib_w2 multi_delib_w2

label define multi_delib_w2 1 "Completely disagree" 2 "Disagree" 3 "Neutral" 4 "Agree" 5 "Completely agree", modify
label values multi_delib_w2 multi_delib_w2
tab multi_delib_w2

********************************************************************************
***** IV I: VIGNETTE TREATMENT ON LINGUISTIC ACCOMMODATIONS ********************
********************************************************************************
* build a treatment variable based on the random assignment of respondents to the 3 different treatment + 1 control group
gen treatment =.
replace treatment = 0 if control == 1
replace treatment = 1 if t1 == 1
replace treatment = 2 if t2 == 1
replace treatment = 3 if t3 == 1
tab treatment 

********************************************************************************
***** IV II: PRE-TREATMENT ATTITUDE TOWARD MULTILINGUAL DMPs *******************
********************************************************************************
** support for multilinguas DMPs in W1 
* recoded into three categories for the subsample analysis
tab multi_delib_w1
recode multi_delib_w1 (6=.) 
label define multi_delib_w1 1 "Completely agree" 2 "Agree" 3 "Neutral" 4 "Disagree" 5 "Completely disagree", modify
label values multi_delib_w1 multi_delib_w1
tab multi_delib_w1

********************************************************************************
***** CONTROL VARIABLES ********************************************************
********************************************************************************

*** CV I: MULTILINGUAL SKILLS & NATIONALITY ************************************

** multilingual skills #1
gen lux_bin=.
replace lux_bin = 0 if luxembourgish < 3
replace lux_bin= 1 if luxembourgish >= 3

gen french_bin=.
replace french_bin = 0 if french < 3
replace french_bin= 1 if french >= 3

gen german_bin=.
replace german_bin = 0 if german < 3
replace german_bin= 1 if german >= 3

gen english_bin=.
replace english_bin = 0 if english < 3
replace english_bin= 1 if english >= 3

gen other_bin=.
replace other_bin = 0 if otherlanguages < 3
replace other_bin= 1 if otherlanguages >= 3

gen multilingual_skills = (lux_bin + french_bin + german_bin + english_bin + other_bin)

** multilingual skills #2
factor luxembourgish french german english otherlanguages
rotate, varimax blanks (.3)
predict factor*
loadingplot
*factor1 = Luxembourgish & German / factor2 = French, English & other languages
rename factor1 native_speakers
rename factor2 foreign_speakers

** nationality
gen nationality=.
replace nationality = 0 if citizenship1 == 2 & citizenship2 == 1
replace nationality = 1 if citizenship1 == 1 & citizenship2 == 1
replace nationality = 2 if citizenship1 == 1 & citizenship2 == 2
label define nationality 0 "non-national" 1 "Luxembourg nationality + other" 2 "Luxembourg nationality only"
label values nationality nationality
tab nationality

*** CV II: SOCIO - DEMO ********************************************************
*sex
tab gender
label drop gender
label define gender 1 "Male" 2 "Female"
label values gender gender 
tab gender

* education
tab edu
recode edu (1=1)(2=1)(3=1)(4=2)(5=2)(6=2)(7=3)(8=3)(9=3)(997=.)
label define edu 1 "Low" 2 "Middle" 3 "High" 
label values edu edu
tab edu
rename edu education

****** CV III: POLITICAL ATTITUDES **********************************************

* political interest
recode interest (1=4) (2=3) (3=2) (4=1) (5=.)
label define interest 1 "not at all interested" 2 "not very interested" 3 "somewhat interested" 4 "very interested", modify
label values interest interest
tab interest

* satisfaction w/ democracy
recode swd (99=.)
tab swd

* left/right self-placement
recode lrsp (99=.)
tab lrsp

* cultural left/right 
recode cult_lr (6=.)(1=5)(2=4)(3=3)(4=2) (5=1)
label define cult_lr 1 "Completely disagree" 2 "Disagree" 3 "Neutral" 4 "Agree" 5 "Completely agree", modify
label values cult_lr cult_lr
tab cult_lr

* internal efficacy 
tab efficacy_perso
recode efficacy_perso (6=.)
label define efficacy_perso 1 "Completely agree" 2 "Agree" 3 "Neutral" 4 "Disagree" 5 "Completely disagree", modify
label values efficacy_perso efficacy_perso
tab efficacy_perso

* trust in representative institutions, 
recode trust1 (6=.)
recode trust2 (6=.)
recode trust3 (6=.)

* build a scale of trust in representative institutions
alpha trust1 trust2 trust3, gen(trust)

****** CV IV: ATTITUDES TOWARDS DMPs in general ********************************

* support for DMPs
tab dmp_support
recode dmp_support (6=.)(1=5)(2=4)(3=3)(4=2)(5=1)
label define dmp_support 1 "Completely disagree" 2 "Disagree" 3 "Neutral" 4 "Agree" 5 "Completely agree", modify
label values dmp_support dmp_support
tab dmp_support

********************************************************************************
***** M2: LINEAR REGRESSION MODELS *********************************************
********************************************************************************

*** FULL SAMPLE ANALYSIS
reg multi_delib_w2 ib(0).treatment 
estimates store all
outreg2 using resultsM2.doc, replace ctitle (M2) alpha(0.001, 0.01, 0.05) stats(coef se )

*** SUBGROUP ANALYSIS 1 based on attitudes toward multilingual DMPs in W1

reg multi_delib_w2 ib(0).treatment if multi_delib_w1 < 3
estimates store negative
outreg2 using resultsM2.doc, append ctitle (M2- sub: negative W1) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib_w2 ib(0).treatment if multi_delib_w1 == 3
estimates store neutral
outreg2 using resultsM2.doc, append ctitle (M2- sub: neutral W1) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib_w2 ib(0).treatment if multi_delib_w1 > 3
estimates store positive
outreg2 using resultsM2.doc, append ctitle (M2- sub: positive W1) alpha(0.001, 0.01, 0.05) stats(coef se )

coefplot all negative neutral positive, xline (0) drop(_cons)

********************************************************************************
***** ROBUSTNESS CHECKS ********************************************************
********************************************************************************

*** Full sample - ordinal, weighted, and w/ controls

* OLS weighted
reg multi_delib_w2 ib(0).treatment [aweight= w2_d_weight]
estimates store all
outreg2 using resultsM2-robust.doc, replace ctitle (M2 - OLS weighted) alpha(0.001, 0.01, 0.05) stats(coef se )

* OLS controls
reg multi_delib_w2 ib(0).treatment multilingual_skills i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1
outreg2 using resultsM2-robust.doc, append ctitle (M2- OLS w/ controls) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib_w2 ib(0).treatment native_speakers foreign_speakers i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1
outreg2 using resultsM2-robust.doc, append ctitle (M2- OLS w/ controls) alpha(0.001, 0.01, 0.05) stats(coef se )

* OLS controls & weighted
reg multi_delib_w2 ib(0).treatment multilingual_skills i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1
outreg2 using resultsM2-robust.doc, append ctitle (M2- OLS w/ controls & weigthed) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib_w2 ib(0).treatment native_speakers foreign_speakers i.nationality gender age education interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1
outreg2 using resultsM2-robust.doc, append ctitle (M2- OLS w/ controls & weighted) alpha(0.001, 0.01, 0.05) stats(coef se )

* Ordinal
ologit multi_delib_w2 ib(0).treatment 
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment

* Ordinal & weighted
ologit multi_delib_w2 ib(0).treatment 
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal, weighted) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment [aweight= w2_d_weight]
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal, wieghted) alpha(0.001, 0.01, 0.05) stats(coef se )

* Oridnal & controls
ologit multi_delib_w2 ib(0).treatment multilingual_skills i.nationality gender age education  interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal, controls ) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment native_speakers foreign_speakers i.nationality gender age education  interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal, controls) alpha(0.001, 0.01, 0.05) stats(coef se )

* Oridnal, controls & weighted
ologit multi_delib_w2 ib(0).treatment multilingual_skills i.nationality gender age education  interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1 [aweight= w2_d_weight]
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal, controls, weighted) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment native_speakers foreign_speakers i.nationality gender age education  interest swd lrsp cult_lr efficacy_perso trust dmp_support multi_delib_w1 [aweight= w2_d_weight]
outreg2 using resultsM2-robust.doc, append ctitle (M2 - ordinal, controls, weighted) alpha(0.001, 0.01, 0.05) stats(coef se )

*** Sub sample 

*** Sub sample - weighted
reg multi_delib_w2 ib(0).treatment if multi_delib_w1 < 3 [aweight= w2_d_weight]
outreg2 using resultsM2-sub_rob.doc, replace ctitle (M2- sub: negative W1, weighted) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib_w2 ib(0).treatment if multi_delib_w1 == 3 [aweight= w2_d_weight]
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: neutral W1, weighted) alpha(0.001, 0.01, 0.05) stats(coef se )
reg multi_delib_w2 ib(0).treatment if multi_delib_w1 > 3 [aweight= w2_d_weight]
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: positive W1, weighted) alpha(0.001, 0.01, 0.05) stats(coef se )

*** Sub sample - ordinal
ologit multi_delib_w2 ib(0).treatment if multi_delib_w1 < 3
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: negative W1, ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment if multi_delib_w1 == 3
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: neutral W1, ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment if multi_delib_w1 > 3
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: positive W1, ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )

*** Sub sample - ordinal & weighted
ologit multi_delib_w2 ib(0).treatment if multi_delib_w1 < 3 [aweight= w2_d_weight]
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: negative W1, ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment if multi_delib_w1 == 3 [aweight= w2_d_weight]
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: neutral W1, ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )
ologit multi_delib_w2 ib(0).treatment if multi_delib_w1 > 3 [aweight= w2_d_weight]
outreg2 using resultsM2-sub_rob.doc, append ctitle (M2- sub: positive W1, ordinal) alpha(0.001, 0.01, 0.05) stats(coef se )

** ANCOVA
anova multi_delib_w2 ib(0).treatment multi_delib_w1 language_sum_bin age gender edu interest efficacy_personal ib(0).treatment#age ib(0).treatment#gender ib(0).treatment#edu ib(0).treatment#multi_delib_w1 ib(0).treatment#language_sum ib(0).treatment#interest ib(0).treatment#efficacy_personal

anova multi_delib_w2 ib(0).treatment multi_delib_w1 language_sum ib(0).treatment#multi_delib_w1 ib(0).treatment#language_sum i.nationality age gender edu interest efficacy_personal swd cult_lr lrsp ib(0).treatment#age ib(0).treatment#gender ib(0).treatment#edu ib(0).treatment#interest ib(0).treatment#efficacy_personal ib(0).treatment#swd ib(0).treatment#cult_lr ib(0).treatment#lrsp ib(0).treatment#i.nationality
